Overview

Dataset statistics

Number of variables17
Number of observations10992
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory136.0 B

Variable types

NUM17

Reproduction

Analysis started2020-08-25 01:43:35.573407
Analysis finished2020-08-25 01:44:19.516583
Duration43.94 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

input1 has 2364 (21.5%) zeros Zeros
input3 has 953 (8.7%) zeros Zeros
input5 has 1568 (14.3%) zeros Zeros
input6 has 397 (3.6%) zeros Zeros
input7 has 988 (9.0%) zeros Zeros
input8 has 1556 (14.2%) zeros Zeros
input9 has 1028 (9.4%) zeros Zeros
input10 has 2239 (20.4%) zeros Zeros
input11 has 1300 (11.8%) zeros Zeros
input12 has 1227 (11.2%) zeros Zeros
input13 has 186 (1.7%) zeros Zeros
input14 has 1756 (16.0%) zeros Zeros
input15 has 2917 (26.5%) zeros Zeros
input16 has 4044 (36.8%) zeros Zeros
target has 1143 (10.4%) zeros Zeros

Variables

input1
Real number (ℝ≥0)

ZEROS

Distinct count101
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.814319505094616
Minimum0.0
Maximum100.0
Zeros2364
Zeros (%)21.5%
Memory size86.0 KiB
2020-08-25T01:44:19.561515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median32
Q365
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)59

Descriptive statistics

Standard deviation34.25778264
Coefficient of variation (CV)0.882606808
Kurtosis-1.041252883
Mean38.81431951
Median Absolute Deviation (MAD)29
Skewness0.50867853
Sum426647
Variance1173.595672
2020-08-25T01:44:19.659154image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0236421.5%
 
100114310.4%
 
321371.2%
 
261291.2%
 
381231.1%
 
191201.1%
 
161201.1%
 
151171.1%
 
231171.1%
 
251171.1%
 
301151.0%
 
201151.0%
 
311141.0%
 
131131.0%
 
291121.0%
 
271121.0%
 
421111.0%
 
241101.0%
 
141091.0%
 
351091.0%
 
331081.0%
 
211081.0%
 
121051.0%
 
361030.9%
 
221020.9%
 
Other values (76)485944.2%
 
ValueCountFrequency (%) 
0236421.5%
 
1590.5%
 
2850.8%
 
3570.5%
 
4790.7%
 
5690.6%
 
6780.7%
 
7910.8%
 
8860.8%
 
9870.8%
 
ValueCountFrequency (%) 
100114310.4%
 
99320.3%
 
98340.3%
 
97450.4%
 
96350.3%
 
95460.4%
 
94280.3%
 
93440.4%
 
92330.3%
 
91320.3%
 

input2
Real number (ℝ≥0)

Distinct count96
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.12026928675401
Minimum0.0
Maximum100.0
Zeros27
Zeros (%)0.2%
Memory size86.0 KiB
2020-08-25T01:44:19.769722image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55
Q176
median89
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)24

Descriptive statistics

Standard deviation16.21857068
Coefficient of variation (CV)0.1905371166
Kurtosis3.050747285
Mean85.12026929
Median Absolute Deviation (MAD)11
Skewness-1.491182875
Sum935642
Variance263.0420348
2020-08-25T01:44:19.869022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
100311528.3%
 
902612.4%
 
912592.4%
 
882542.3%
 
962472.2%
 
932452.2%
 
852452.2%
 
892442.2%
 
942422.2%
 
822362.1%
 
922352.1%
 
952342.1%
 
862322.1%
 
872212.0%
 
972172.0%
 
812162.0%
 
832131.9%
 
842051.9%
 
982031.8%
 
802001.8%
 
782001.8%
 
771751.6%
 
991721.6%
 
761721.6%
 
791671.5%
 
Other values (71)258223.5%
 
ValueCountFrequency (%) 
0270.2%
 
32< 0.1%
 
42< 0.1%
 
52< 0.1%
 
61< 0.1%
 
81< 0.1%
 
91< 0.1%
 
111< 0.1%
 
123< 0.1%
 
132< 0.1%
 
ValueCountFrequency (%) 
100311528.3%
 
991721.6%
 
982031.8%
 
972172.0%
 
962472.2%
 
952342.1%
 
942422.2%
 
932452.2%
 
922352.1%
 
912592.4%
 

input3
Real number (ℝ≥0)

ZEROS

Distinct count101
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.60562227074236
Minimum0.0
Maximum100.0
Zeros953
Zeros (%)8.7%
Memory size86.0 KiB
2020-08-25T01:44:19.981206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median40
Q358
95-th percentile89
Maximum100
Range100
Interquartile range (IQR)38

Descriptive statistics

Standard deviation26.34298414
Coefficient of variation (CV)0.648752135
Kurtosis-0.5956949956
Mean40.60562227
Median Absolute Deviation (MAD)19
Skewness0.2684032131
Sum446337
Variance693.9528134
2020-08-25T01:44:20.088930image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
09538.7%
 
1003092.8%
 
351851.7%
 
461771.6%
 
391681.5%
 
341671.5%
 
401671.5%
 
381661.5%
 
441611.5%
 
431591.4%
 
531571.4%
 
421571.4%
 
521571.4%
 
301521.4%
 
571501.4%
 
471501.4%
 
561491.4%
 
411481.3%
 
541461.3%
 
501451.3%
 
511441.3%
 
491431.3%
 
311421.3%
 
601411.3%
 
551411.3%
 
Other values (76)615856.0%
 
ValueCountFrequency (%) 
09538.7%
 
1750.7%
 
2770.7%
 
3730.7%
 
4790.7%
 
5840.8%
 
6830.8%
 
7790.7%
 
81030.9%
 
9910.8%
 
ValueCountFrequency (%) 
1003092.8%
 
99170.2%
 
98240.2%
 
97230.2%
 
96210.2%
 
95280.3%
 
94160.1%
 
93260.2%
 
92280.3%
 
91200.2%
 

input4
Real number (ℝ≥0)

Distinct count98
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.77419941775837
Minimum0.0
Maximum100.0
Zeros14
Zeros (%)0.1%
Memory size86.0 KiB
2020-08-25T01:44:20.206411image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47
Q172
median91
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)28

Descriptive statistics

Standard deviation19.16364639
Coefficient of variation (CV)0.2287535604
Kurtosis1.104591938
Mean83.77419942
Median Absolute Deviation (MAD)9
Skewness-1.193760628
Sum920846
Variance367.2453428
2020-08-25T01:44:20.305938image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
100438739.9%
 
751731.6%
 
781721.6%
 
771711.6%
 
811711.6%
 
801671.5%
 
791671.5%
 
991621.5%
 
821611.5%
 
761601.5%
 
831581.4%
 
981531.4%
 
841471.3%
 
741461.3%
 
731431.3%
 
971361.2%
 
711351.2%
 
901341.2%
 
701301.2%
 
851291.2%
 
681271.2%
 
961261.1%
 
861261.1%
 
691241.1%
 
921231.1%
 
Other values (73)306427.9%
 
ValueCountFrequency (%) 
0140.1%
 
24< 0.1%
 
43< 0.1%
 
61< 0.1%
 
71< 0.1%
 
85< 0.1%
 
95< 0.1%
 
105< 0.1%
 
114< 0.1%
 
123< 0.1%
 
ValueCountFrequency (%) 
100438739.9%
 
991621.5%
 
981531.4%
 
971361.2%
 
961261.1%
 
951081.0%
 
941020.9%
 
931121.0%
 
921231.1%
 
911151.0%
 

input5
Real number (ℝ≥0)

ZEROS

Distinct count101
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.77037845705968
Minimum0.0
Maximum100.0
Zeros1568
Zeros (%)14.3%
Memory size86.0 KiB
2020-08-25T01:44:20.416197image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median53
Q378
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)60

Descriptive statistics

Standard deviation34.10051458
Coefficient of variation (CV)0.685156827
Kurtosis-1.310362223
Mean49.77037846
Median Absolute Deviation (MAD)30
Skewness-0.07887461935
Sum547076
Variance1162.845095
2020-08-25T01:44:20.524017image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0156814.3%
 
100115510.5%
 
661591.4%
 
681301.2%
 
651281.2%
 
621271.2%
 
601201.1%
 
641141.0%
 
671141.0%
 
611131.0%
 
701121.0%
 
741111.0%
 
731111.0%
 
761101.0%
 
691071.0%
 
811061.0%
 
591051.0%
 
521051.0%
 
781051.0%
 
221040.9%
 
771020.9%
 
711020.9%
 
571020.9%
 
631020.9%
 
56990.9%
 
Other values (76)568151.7%
 
ValueCountFrequency (%) 
0156814.3%
 
1680.6%
 
2610.6%
 
3390.4%
 
4550.5%
 
5640.6%
 
6690.6%
 
7670.6%
 
8720.7%
 
9610.6%
 
ValueCountFrequency (%) 
100115510.5%
 
99430.4%
 
98410.4%
 
97600.5%
 
96690.6%
 
95690.6%
 
94620.6%
 
93660.6%
 
92840.8%
 
91720.7%
 

input6
Real number (ℝ≥0)

ZEROS

Distinct count101
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.57314410480349
Minimum0.0
Maximum100.0
Zeros397
Zeros (%)3.6%
Memory size86.0 KiB
2020-08-25T01:44:20.640614image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q149
median71
Q386
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)37

Descriptive statistics

Standard deviation26.99668765
Coefficient of variation (CV)0.4117034194
Kurtosis-0.1699343809
Mean65.5731441
Median Absolute Deviation (MAD)18
Skewness-0.7405550347
Sum720780
Variance728.8211439
2020-08-25T01:44:20.738677image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
100111510.1%
 
03973.6%
 
782111.9%
 
752071.9%
 
792051.9%
 
722001.8%
 
761931.8%
 
771841.7%
 
741831.7%
 
731801.6%
 
701781.6%
 
711761.6%
 
801761.6%
 
821741.6%
 
681731.6%
 
831701.5%
 
671701.5%
 
811691.5%
 
691631.5%
 
881511.4%
 
661431.3%
 
651401.3%
 
851401.3%
 
991371.2%
 
911341.2%
 
Other values (76)552350.2%
 
ValueCountFrequency (%) 
03973.6%
 
1240.2%
 
2250.2%
 
3120.1%
 
4170.2%
 
5240.2%
 
6210.2%
 
7260.2%
 
8190.2%
 
9230.2%
 
ValueCountFrequency (%) 
100111510.1%
 
991371.2%
 
981291.2%
 
971271.2%
 
961101.0%
 
951251.1%
 
941321.2%
 
931071.0%
 
921051.0%
 
911341.2%
 

input7
Real number (ℝ≥0)

ZEROS

Distinct count101
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.220251091703055
Minimum0.0
Maximum100.0
Zeros988
Zeros (%)9.0%
Memory size86.0 KiB
2020-08-25T01:44:20.845446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q128
median53.5
Q374
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.57688117
Coefficient of variation (CV)0.5969685919
Kurtosis-1.006198581
Mean51.22025109
Median Absolute Deviation (MAD)22.5
Skewness-0.1340645608
Sum563013
Variance934.9456621
2020-08-25T01:44:20.948214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
09889.0%
 
1008998.2%
 
681551.4%
 
561451.3%
 
701421.3%
 
611421.3%
 
531391.3%
 
671391.3%
 
571391.3%
 
621381.3%
 
691361.2%
 
541351.2%
 
511341.2%
 
551341.2%
 
721331.2%
 
661331.2%
 
651311.2%
 
501281.2%
 
741271.2%
 
591271.2%
 
521261.1%
 
631251.1%
 
601241.1%
 
411241.1%
 
731181.1%
 
Other values (76)603154.9%
 
ValueCountFrequency (%) 
09889.0%
 
1380.3%
 
2680.6%
 
3500.5%
 
4600.5%
 
5530.5%
 
6610.6%
 
7680.6%
 
8570.5%
 
9530.5%
 
ValueCountFrequency (%) 
1008998.2%
 
99510.5%
 
98400.4%
 
97360.3%
 
96430.4%
 
95570.5%
 
94560.5%
 
93610.6%
 
92540.5%
 
91640.6%
 

input8
Real number (ℝ≥0)

ZEROS

Distinct count101
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.49899927219796
Minimum0.0
Maximum100.0
Zeros1556
Zeros (%)14.2%
Memory size86.0 KiB
2020-08-25T01:44:21.063734image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123
median43
Q364
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)41

Descriptive statistics

Standard deviation29.90610361
Coefficient of variation (CV)0.6720623856
Kurtosis-0.8258615147
Mean44.49899927
Median Absolute Deviation (MAD)21
Skewness0.1809040796
Sum489133
Variance894.3750331
2020-08-25T01:44:21.168301image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0155614.2%
 
1007286.6%
 
351831.7%
 
341771.6%
 
401761.6%
 
391751.6%
 
431731.6%
 
361691.5%
 
381671.5%
 
331591.4%
 
371521.4%
 
311471.3%
 
521461.3%
 
581451.3%
 
601451.3%
 
551441.3%
 
421421.3%
 
501421.3%
 
531421.3%
 
321401.3%
 
411381.3%
 
621361.2%
 
611321.2%
 
571311.2%
 
511291.2%
 
Other values (76)521847.5%
 
ValueCountFrequency (%) 
0155614.2%
 
1400.4%
 
2310.3%
 
3390.4%
 
4390.4%
 
5420.4%
 
6360.3%
 
7300.3%
 
8390.4%
 
9460.4%
 
ValueCountFrequency (%) 
1007286.6%
 
99390.4%
 
98470.4%
 
97330.3%
 
96410.4%
 
95360.3%
 
94470.4%
 
93460.4%
 
92530.5%
 
91340.3%
 

input9
Real number (ℝ≥0)

ZEROS

Distinct count101
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.86854075691412
Minimum0.0
Maximum100.0
Zeros1028
Zeros (%)9.4%
Memory size86.0 KiB
2020-08-25T01:44:21.283366image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q129
median60
Q389
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)60

Descriptive statistics

Standard deviation34.13552955
Coefficient of variation (CV)0.6002533051
Kurtosis-1.249262485
Mean56.86854076
Median Absolute Deviation (MAD)30
Skewness-0.2704547505
Sum625099
Variance1165.234377
2020-08-25T01:44:21.387213image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
100176416.0%
 
010289.4%
 
841271.2%
 
881241.1%
 
711131.0%
 
941131.0%
 
791131.0%
 
411091.0%
 
601091.0%
 
821081.0%
 
851061.0%
 
951061.0%
 
771051.0%
 
781030.9%
 
421030.9%
 
701020.9%
 
811020.9%
 
621020.9%
 
861010.9%
 
931010.9%
 
48990.9%
 
46990.9%
 
67980.9%
 
47980.9%
 
92980.9%
 
Other values (76)576152.4%
 
ValueCountFrequency (%) 
010289.4%
 
1550.5%
 
2450.4%
 
3570.5%
 
4480.4%
 
5600.5%
 
6560.5%
 
7510.5%
 
8640.6%
 
9390.4%
 
ValueCountFrequency (%) 
100176416.0%
 
99840.8%
 
98780.7%
 
97850.8%
 
96880.8%
 
951061.0%
 
941131.0%
 
931010.9%
 
92980.9%
 
91970.9%
 

input10
Real number (ℝ≥0)

ZEROS

Distinct count101
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.69596069868996
Minimum0.0
Maximum100.0
Zeros2239
Zeros (%)20.4%
Memory size86.0 KiB
2020-08-25T01:44:21.497254image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median33
Q354
95-th percentile79
Maximum100
Range100
Interquartile range (IQR)47

Descriptive statistics

Standard deviation27.25154828
Coefficient of variation (CV)0.8087482214
Kurtosis-0.9620598218
Mean33.6959607
Median Absolute Deviation (MAD)24
Skewness0.3436149067
Sum370386
Variance742.6468839
2020-08-25T01:44:21.600684image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0223920.4%
 
752372.2%
 
351791.6%
 
441591.4%
 
361511.4%
 
451471.3%
 
431401.3%
 
341401.3%
 
421391.3%
 
411381.3%
 
401381.3%
 
381371.2%
 
491331.2%
 
481331.2%
 
291321.2%
 
301311.2%
 
471301.2%
 
461281.2%
 
161231.1%
 
321221.1%
 
391221.1%
 
501221.1%
 
371211.1%
 
121171.1%
 
251131.0%
 
Other values (76)542149.3%
 
ValueCountFrequency (%) 
0223920.4%
 
1790.7%
 
2720.7%
 
3650.6%
 
4910.8%
 
5910.8%
 
61051.0%
 
7770.7%
 
8960.9%
 
9870.8%
 
ValueCountFrequency (%) 
100690.6%
 
9990.1%
 
9880.1%
 
9760.1%
 
96130.1%
 
95110.1%
 
94130.1%
 
93100.1%
 
92150.1%
 
91230.2%
 

input11
Real number (ℝ≥0)

ZEROS

Distinct count101
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.51637554585153
Minimum0.0
Maximum100.0
Zeros1300
Zeros (%)11.8%
Memory size86.0 KiB
2020-08-25T01:44:21.713741image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123
median73
Q397
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)74

Descriptive statistics

Standard deviation37.28808567
Coefficient of variation (CV)0.616165217
Kurtosis-1.332298912
Mean60.51637555
Median Absolute Deviation (MAD)27
Skewness-0.476634663
Sum665196
Variance1390.401333
2020-08-25T01:44:21.817534image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
100248222.6%
 
0130011.8%
 
911331.2%
 
881331.2%
 
871301.2%
 
861291.2%
 
921271.2%
 
781241.1%
 
821241.1%
 
901221.1%
 
851201.1%
 
811161.1%
 
931151.0%
 
951151.0%
 
891151.0%
 
971141.0%
 
961131.0%
 
771131.0%
 
981121.0%
 
941111.0%
 
761091.0%
 
801091.0%
 
741091.0%
 
831071.0%
 
791030.9%
 
Other values (76)450741.0%
 
ValueCountFrequency (%) 
0130011.8%
 
1530.5%
 
2660.6%
 
3480.4%
 
4790.7%
 
5860.8%
 
6760.7%
 
7580.5%
 
8570.5%
 
9700.6%
 
ValueCountFrequency (%) 
100248222.6%
 
991010.9%
 
981121.0%
 
971141.0%
 
961131.0%
 
951151.0%
 
941111.0%
 
931151.0%
 
921271.2%
 
911331.2%
 

input12
Real number (ℝ≥0)

ZEROS

Distinct count101
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.82651018922853
Minimum0.0
Maximum100.0
Zeros1227
Zeros (%)11.2%
Memory size86.0 KiB
2020-08-25T01:44:21.930565image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median30
Q355
95-th percentile84
Maximum100
Range100
Interquartile range (IQR)44

Descriptive statistics

Standard deviation27.11998189
Coefficient of variation (CV)0.7787166082
Kurtosis-0.7419427954
Mean34.82651019
Median Absolute Deviation (MAD)21
Skewness0.4819380908
Sum382813
Variance735.4934178
2020-08-25T01:44:22.036251image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0122711.2%
 
502242.0%
 
1001921.7%
 
161911.7%
 
151821.7%
 
91681.5%
 
181671.5%
 
171611.5%
 
121611.5%
 
201561.4%
 
191551.4%
 
141541.4%
 
81541.4%
 
101531.4%
 
511521.4%
 
11491.4%
 
211471.3%
 
131471.3%
 
111451.3%
 
241381.3%
 
221381.3%
 
231371.2%
 
491351.2%
 
301351.2%
 
291301.2%
 
Other values (76)599454.5%
 
ValueCountFrequency (%) 
0122711.2%
 
11491.4%
 
21211.1%
 
31271.2%
 
41261.1%
 
51261.1%
 
61301.2%
 
71281.2%
 
81541.4%
 
91681.5%
 
ValueCountFrequency (%) 
1001921.7%
 
99140.1%
 
98140.1%
 
97130.1%
 
96140.1%
 
95250.2%
 
94240.2%
 
93260.2%
 
92190.2%
 
91190.2%
 

input13
Real number (ℝ≥0)

ZEROS

Distinct count101
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.02228893740902
Minimum0.0
Maximum100.0
Zeros186
Zeros (%)1.7%
Memory size86.0 KiB
2020-08-25T01:44:22.147007image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q142
median53
Q368
95-th percentile98
Maximum100
Range100
Interquartile range (IQR)26

Descriptive statistics

Standard deviation22.3355392
Coefficient of variation (CV)0.4059362057
Kurtosis-0.06514140816
Mean55.02228894
Median Absolute Deviation (MAD)13
Skewness0.02617322521
Sum604805
Variance498.8763115
2020-08-25T01:44:22.246015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1005094.6%
 
504434.0%
 
513132.8%
 
492982.7%
 
532562.3%
 
522552.3%
 
482502.3%
 
552252.0%
 
462182.0%
 
562172.0%
 
542091.9%
 
602081.9%
 
582051.9%
 
452051.9%
 
622001.8%
 
571991.8%
 
411921.7%
 
471921.7%
 
01861.7%
 
431841.7%
 
421821.7%
 
441811.6%
 
611711.6%
 
381611.5%
 
401571.4%
 
Other values (76)517647.1%
 
ValueCountFrequency (%) 
01861.7%
 
190.1%
 
2130.1%
 
3190.2%
 
4160.1%
 
5140.1%
 
6150.1%
 
7240.2%
 
8260.2%
 
9250.2%
 
ValueCountFrequency (%) 
1005094.6%
 
99190.2%
 
98340.3%
 
97340.3%
 
96400.4%
 
95410.4%
 
94500.5%
 
93480.4%
 
92580.5%
 
91750.7%
 

input14
Real number (ℝ≥0)

ZEROS

Distinct count101
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.93704512372635
Minimum0.0
Maximum100.0
Zeros1756
Zeros (%)16.0%
Memory size86.0 KiB
2020-08-25T01:44:22.569196image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median27
Q347
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)42

Descriptive statistics

Standard deviation33.155463
Coefficient of variation (CV)0.9490059302
Kurtosis-0.5326653718
Mean34.93704512
Median Absolute Deviation (MAD)21
Skewness0.853667371
Sum384028
Variance1099.284727
2020-08-25T01:44:22.673800image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0175616.0%
 
1008948.1%
 
252942.7%
 
12462.2%
 
22332.1%
 
322332.1%
 
242312.1%
 
342041.9%
 
272021.8%
 
42011.8%
 
261991.8%
 
281991.8%
 
231891.7%
 
291861.7%
 
311841.7%
 
301831.7%
 
31621.5%
 
331591.4%
 
51591.4%
 
221551.4%
 
351551.4%
 
61441.3%
 
361431.3%
 
71371.2%
 
201371.2%
 
Other values (76)400736.5%
 
ValueCountFrequency (%) 
0175616.0%
 
12462.2%
 
22332.1%
 
31621.5%
 
42011.8%
 
51591.4%
 
61441.3%
 
71371.2%
 
81141.0%
 
91010.9%
 
ValueCountFrequency (%) 
1008948.1%
 
99820.7%
 
98770.7%
 
97790.7%
 
96730.7%
 
95960.9%
 
94810.7%
 
93680.6%
 
92650.6%
 
91660.6%
 

input15
Real number (ℝ≥0)

ZEROS

Distinct count101
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.28748180494905
Minimum0.0
Maximum100.0
Zeros2917
Zeros (%)26.5%
Memory size86.0 KiB
2020-08-25T01:44:22.788255image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median40
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)100

Descriptive statistics

Standard deviation41.76039983
Coefficient of variation (CV)0.8831174391
Kurtosis-1.685793174
Mean47.2874818
Median Absolute Deviation (MAD)40
Skewness0.146745718
Sum519784
Variance1743.930994
2020-08-25T01:44:22.894783image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
100298827.2%
 
0291726.5%
 
7960.9%
 
8890.8%
 
5860.8%
 
9860.8%
 
6830.8%
 
16820.7%
 
10810.7%
 
19770.7%
 
11770.7%
 
3740.7%
 
12730.7%
 
15730.7%
 
24710.6%
 
29700.6%
 
21700.6%
 
14700.6%
 
22690.6%
 
18680.6%
 
13680.6%
 
25660.6%
 
88660.6%
 
20640.6%
 
2630.6%
 
Other values (76)336530.6%
 
ValueCountFrequency (%) 
0291726.5%
 
1550.5%
 
2630.6%
 
3740.7%
 
4440.4%
 
5860.8%
 
6830.8%
 
7960.9%
 
8890.8%
 
9860.8%
 
ValueCountFrequency (%) 
100298827.2%
 
99220.2%
 
98390.4%
 
97420.4%
 
96290.3%
 
95320.3%
 
94430.4%
 
93390.4%
 
92420.4%
 
91440.4%
 

input16
Real number (ℝ≥0)

ZEROS

Distinct count101
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.845342066957787
Minimum0.0
Maximum100.0
Zeros4044
Zeros (%)36.8%
Memory size86.0 KiB
2020-08-25T01:44:23.013277image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q351
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)51

Descriptive statistics

Standard deviation35.77809351
Coefficient of variation (CV)1.240342147
Kurtosis-0.6535576435
Mean28.84534207
Median Absolute Deviation (MAD)9
Skewness0.9482646831
Sum317068
Variance1280.071975
2020-08-25T01:44:23.118362image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0404436.8%
 
1007596.9%
 
12282.1%
 
21851.7%
 
51751.6%
 
31741.6%
 
41641.5%
 
71511.4%
 
61461.3%
 
81411.3%
 
121111.0%
 
101071.0%
 
91030.9%
 
111010.9%
 
19990.9%
 
13930.8%
 
16880.8%
 
98850.8%
 
15850.8%
 
99850.8%
 
14830.8%
 
38720.7%
 
37710.6%
 
17700.6%
 
20700.6%
 
Other values (76)350231.9%
 
ValueCountFrequency (%) 
0404436.8%
 
12282.1%
 
21851.7%
 
31741.6%
 
41641.5%
 
51751.6%
 
61461.3%
 
71511.4%
 
81411.3%
 
91030.9%
 
ValueCountFrequency (%) 
1007596.9%
 
99850.8%
 
98850.8%
 
97640.6%
 
96630.6%
 
95670.6%
 
94640.6%
 
93440.4%
 
92560.5%
 
91540.5%
 

target
Real number (ℝ≥0)

ZEROS

Distinct count10
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.431586608442504
Minimum0
Maximum9
Zeros1143
Zeros (%)10.4%
Memory size86.0 KiB
2020-08-25T01:44:23.225266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.876946685
Coefficient of variation (CV)0.6491911226
Kurtosis-1.232673067
Mean4.431586608
Median Absolute Deviation (MAD)3
Skewness0.02615254632
Sum48712
Variance8.27682223
2020-08-25T01:44:23.346346image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4114410.4%
 
2114410.4%
 
1114310.4%
 
0114310.4%
 
7114210.4%
 
610569.6%
 
510559.6%
 
310559.6%
 
910559.6%
 
810559.6%
 
ValueCountFrequency (%) 
0114310.4%
 
1114310.4%
 
2114410.4%
 
310559.6%
 
4114410.4%
 
510559.6%
 
610569.6%
 
7114210.4%
 
810559.6%
 
910559.6%
 
ValueCountFrequency (%) 
910559.6%
 
810559.6%
 
7114210.4%
 
610569.6%
 
510559.6%
 
4114410.4%
 
310559.6%
 
2114410.4%
 
1114310.4%
 
0114310.4%
 

Interactions

2020-08-25T01:43:37.053964image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:37.192389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:37.333308image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:37.661006image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:37.804779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:37.945467image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:38.082296image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:38.223407image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:38.362927image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:38.502185image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:38.640665image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:38.784415image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:38.928214image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:39.067627image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:39.205381image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:39.348369image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:39.490736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:39.628256image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:39.775222image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:39.923241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:40.062525image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:40.200216image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:40.338340image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:40.473843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:40.619606image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:40.757236image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:40.894584image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:41.030505image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:41.165713image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:41.301339image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:41.437707image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:41.572507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:41.707514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:41.844206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:41.975982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:42.111123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:42.457486image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:42.593984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:42.729916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:42.873510image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:43.014368image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:43.153502image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:43.295779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:43.433829image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:43.569735image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:43.706957image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:43.857287image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:43.998394image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:44.141023image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:44.281410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:44.425659image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:44.560512image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:44.698027image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:44.832965image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:44.974666image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:45.132937image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:45.271409image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:45.413116image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:45.550365image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:45.687602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:45.826446image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:45.964164image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:46.105379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:46.240068image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:46.377736image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:46.516906image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:46.658324image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:46.799897image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:47.129737image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:47.266608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:47.406114image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:47.549155image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:47.690533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:47.829138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:47.964727image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:48.101912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:48.238485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:48.376053image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:43:48.519008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
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2020-08-25T01:44:18.078926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:44:18.206485image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:44:18.340071image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:44:18.472094image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-08-25T01:44:23.485081image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-25T01:44:23.781716image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-25T01:44:24.075165image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-25T01:44:24.371746image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-25T01:44:18.748915image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-08-25T01:44:19.326798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

input1input2input3input4input5input6input7input8input9input10input11input12input13input14input15input16target
047.0100.027.081.057.037.026.00.00.023.056.053.0100.090.040.098.08
10.089.027.0100.042.075.029.045.015.015.037.00.069.02.0100.06.02
20.057.031.068.072.090.0100.0100.076.075.050.051.028.025.016.00.01
30.0100.07.092.05.068.019.045.086.034.0100.045.074.023.067.00.04
40.067.049.083.0100.0100.081.080.060.060.040.040.033.020.047.00.01
5100.0100.088.099.049.074.017.047.00.016.037.00.073.016.020.020.06
60.0100.03.072.026.035.085.035.0100.071.073.097.065.049.066.00.04
70.039.02.062.011.05.063.00.0100.043.089.099.036.0100.00.057.00
813.089.012.050.072.038.056.00.04.017.00.061.032.094.0100.0100.05
957.0100.022.072.00.031.025.00.075.013.0100.050.075.087.026.085.00

Last rows

input1input2input3input4input5input6input7input8input9input10input11input12input13input14input15input16target
109820.085.021.0100.066.098.083.077.090.053.0100.030.075.010.035.00.03
109830.063.016.090.0100.0100.090.073.053.040.022.06.011.00.048.018.07
109840.074.035.0100.0100.096.094.063.052.035.00.011.05.00.077.06.02
1098543.089.093.0100.066.091.040.064.082.043.0100.015.052.00.00.01.05
1098643.081.011.069.078.051.0100.017.027.00.00.032.014.080.084.0100.05
1098736.0100.024.070.00.038.049.033.095.047.087.055.096.021.0100.00.04
1098816.075.041.0100.052.064.032.027.00.00.021.09.062.02.0100.014.02
1098956.0100.027.079.00.039.012.00.066.015.0100.051.093.093.038.093.00
1099019.0100.00.061.03.023.048.00.097.027.0100.066.062.097.010.081.00
1099138.0100.037.081.012.055.00.028.052.027.0100.042.086.026.065.00.04